• European Marine Science

The data contains improved daily MODIS Terra/Aqua combined snow-cover merged with Randolph Glacier Inventory (RGI6.0) product for the year 2020. The data is associated with the product named as M*D10A1GL06. The data covers High Mountain Asia (HMA) covering latitude 24.32− 49.19 N and Longitude 58.22 - 122.48 E. The data is available in GeoTIFF file format. For more details about the data, please read the paper titled An improved Terra-Aqua MODIS daily cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019 in Earth System Science Data Journal. The data contains snow data for the period between 2019 and 2021. The data contains 8-day composite snow and each image has the information of no snow (value 25), clouds (value 50), and snow (value 200).

La mission effectuée du 13 mai au 7 juin 90 à l'écloserie de la ferme Sodacal en Nouvelle-Calédonie avait pour but de tester les aqua-plakR pour les comptages bactériens en routine dans une écloserie industrielle. La technique classique (comptage sur boite de Pétri) est en effet très peu utilisée par les éleveurs du fait de sa lourdeur alors qu'une bonne connaissance de l'évolution de la flore bactérienne dans les bacs d'élevages peut s'avérer nécessaire pour assurer une gestion sanitaire correcte de ces élevages. D'autre part, une collection la plus large possible des différentes souches bactériennes rencontrées a été réalisée. Le présent rapport n'aborde que l'aspect utilisation des aqua-plakR, les souches bactériennes étant actuellement en cours d'identification. Un rapport final abordera plus l'aspect qualitatif de la flore bactérienne rencontrée et son éventuel répercution sur les élevages larvaires.

Keberadaan daerah penangkapan ikan di perairan akan selalu bersifat dinamis, selalu berubah atau berpindah mengikuti pergerakan kondisi lingkungan, yang secara alamiah ikan akan memilih habitat yang lebih sesuai. Zona tangkapan ikan Tuna yang diprediksi dapat dilakukan dengan mendeteksi distribusi klorofil-a dan distribusi suhu permukaan laut dari citra Aqua MODIS. Penelitian ini bertujuan untuk memprediksi zona lokal penangkapan ikan tuna di laut sekitar kota Bitung, berdasarkan distribusi klorofil-a dan suhu permukaan laut dengan menggunakan citra satelit Aqua MODIS data level-3. Serangkaian kegiatan penelitian yang dilakukan secara bertahap adalah: koleksi gambar, pemotongan gambar sesuai dengan area yang diinginkan, ekstraksi gambar, interpolasi data, overlay peta, dan terakhir adalah tata letak peta. Hasil dari Suhu Permukaan Laut (SST) dan konsentrasi klorofil-a di perairan laut Bitung dan sekitarnya menunjukkan klorofil-a dan suhu permukaan laut bervariasi setiap musim. Distribusi klorofil-a tertinggi adalah pada musim peralihan kedua pada bulan September dan terendah di musim barat pada bulan Desember. Distribusi suhu permukaan laut tertinggi adalah di musim timur pada bulan Juni dan terendah di musim timur pada bulan Agustus. Hasil penelitian menunjukkan pada beberapa titik penangkapan ikan Tuna yang paling potensial pada musim peralihan II dimana setiap bulan di musim tersebut berpotensi membentuk daerah penangkapan Tuna.

Atmospheric dust particles originating in the arid and semi arid regions of the world are known to be principal sources of mineral dust. The use of satellite remote sensing dust, the potential of this technique is created to provide valuable information to assist in the design of network measurement and estimation dust in marine environments. Dust deposited provides key nutrients such as iron to oceanic phytoplankton. Aerosol optical depth were reviewed in the study between March 2008 and December 2013 in the Persian Gulf. Aqua and Terra satellites for the MODIS sensor data as well as aerosol data (PM10) and Environmental stations and optical depth stations AERONET, used to evaluate the aerosol optical depth. The results showed that the data of MODIS AOD has acceptable accuracy and very high correlation between the values measured by MODIS and network AERONET, there (correlation coefficient: 90/0). Comparison between AOD values derived from measurements by satellites Aqua and Terra MODIS sensor and the amount of aerosol (PM10) estimated environmental stations in the Persian Gulf region also took place. The results showed that between these two values correlated to the Aqua and Terra satellites in the study area, and the correlation coefficient was greater in summer than winter. The results of this study showed that the optical depth data from the MODIS satellite images can provide accurate information dusts the Persian Gulf. Published

Here we present a dataset of Transient Snowline Altitude (TSLA) measurements for glaciers in High Mountain Asia (HMA) based on Landsat satellite imagery and digital elevation model data. The data were obtained using the MountAiN glacier Transient snowline Retrieval Algorithm (MANTRA), a Google Earth Engine tool to measure the average altitude of the snow-ice boundary. Each MANTRA result consists of reference data (e.g. Landsat scene, date, glacier ID), relevant topographic metrics (glacier area, minimum and maximum elevation of the glacier), results of the surface material classification (areas covered by ice, snow, debris and clouds), summary statistics of the TSLA measurement, and quality metrics (cloud cover close to snow-ice boundary, class coverage). For the dataset presented here, we applied MANTRA to all glaciers in HMA with an area larger than 0.5 km² (ca. 28,500 based on Randolph Glacier Inventory v6 glacier outlines). After filtering and postprocessing, the dataset comprises ca. 9.66 million TSLA measurements with an average of 341 ± 160 measurements per glacier, covering the time span 1985 to 2021. Time series of Transient Snowline Altitude (TSLA) metrics for glaciers in High Mountain Asia, 1986 to 2021.The file is in NetCDF format, with the date of the Landsat measurement (LS_DATE) as index.Individual glacier are identified through Randolph Glacier Inventory v6 IDs (RGI_ID).The recommended metric to use for analyses is the median elevation of the detected TSLA range (TSLrange_median_masl).

Aqua is a brand of Bottled Drinking Water (bottled water) which became one of the favorites in Indonesia. Brand loyalty is a positive attitude in making purchases over to a brand over time despite situational influences and marketing efforts have the potential to lead to brand switching. There are several variables that affect brand loyalty, such as brand trust, customer satisfaction, and Corporate Social Responsibility (CSR). This study aimed to determine the influence of brand trust, customer satisfaction, and brand loyalty on CSR to Aqua in Denpasar City. The sample used there are 120 respondents with purposive sampling method. The data analysis technique used is multiple linear regression analysis techniques. Based on the results of analysis show that partially brand trust and CSR are positive effect and significant on brand loyalty. As for the customer satisfaction is positive effect and not significant on brand loyalty.

Salah satu dampak pemanasan global adalah terjadinya perubahan iklim yang signifikan. Perubahan iklim yang terjadi mengakibatkan bencana hidro-meteorologi yaitu kekeringan dimana salah satu faktor terjadinya adalah peningkatan suhu permukaan tanah. Data suhu permukaan tanah di Indonesia diperoleh dari stasiun pengamat cuaca yang didapatkan dengan menggunakan termometer yang dipasang dalam sangkar cuaca. Data yang diperoleh dari pengamatan termometer ini hanya mewakili daerah sekitar.Dalam penelitian ini, data suhu permukaan tanah didapat dengan mengunakan metode penginderaan jauh dengan memanfaatkan data citra satelit Terra Moderate Resolution Imaging Spectroradiometer (MODIS) dan Aqua MODIS serta menggunakan algoritma Li & Becker. Penelitian ini dilakukan di daerah Kabupaten Malang dan Surabaya.Hasil dari penelitian menunjukkan bahwa selama tahun 2008-2010 terjadi perubahan suhu permukaan tanah di Kabupaten Malang dan Surabya secara dinamis. Dari perbandingan antara data hasil pengukuran lapangan dengan hasil pengolahan data citra satelit MODIS diperoleh nilai koefisien determinasi (R2) = 0,4774 dan nilai korelasi (R) = 0,6909 (69,09%) dengan nilai RMSE = 3,6 0C untuk data citra satelit Terra MODIS serta R2 = 0,6451 dan R = 0,7906 (79,06%) dengan nilai RMSE = 6,4 0C untuk data citra satelit Aqua MODIS.

Sea surface temperature (SST) is one of the parameters which can be used to detect the potential of fish distribution in the sea. One of method which can be used to measure the SST to utilize remote sensing satellite imagery. The data used in this study are the Aqua, Terra and NOAA satellite imagery from 2010 until 2012. This study purpose is to find out the value of SST in the northern region of Papua in 2010 to 2012 from satellite imagery and also to compare the satellite imagery with Buoy data as a field validation data.The methode of processing is used a script programming language by using the programming software which built to get the SST value by compiling data. The next step when the previous processing result has out are selecting the data, emulating the time between the satellite imagery with the Buoy Data, average monthly and yearly SST calculation, noise and RMSE value calculation, making the graphic and scatterplot, and also the depiction of SST distribution maps.The result shows the value of SST in northern Papua has decreased every year with a random pattern. From the three satellite imagery which are used in this research, NOAA imagery is the most imagery which can represent the condition of the SST on real field. It is due to the value of the noise and RMSE on NOAA are about -0.43 and 0.2228. Based on statistic test, there is a correlation between the Aqua, Terra and NOAA imagery and Buoy Data. Then, there is a difference between the value of the average SST temperature data from satellite imagery and Buoy with a confidence level of 95%.